ID: 1948

Development and Evaluation of Speech Synthesis System Based on Deep Learning Models

This study concentrates on the investigation, development, and evaluation of Text-to-Speech Synthesis systems based on Deep Learning models for the Azerbaijani Language. We have selected and compared state-of-the-art models-Tacotron and Deep Convolutional Text-to-Speech (DC TTS) systems to achieve the most optimal model. Both systems were trained on the 24 h speech dataset of the Azerbaijani language collected and processed from the news website. To analyze the quality and intelligibility of the speech signals produced by two systems, 34 listeners participated in an online survey containing subjective evaluation tests. The results of the study indicated that according to the Mean Opinion Score, Tacotron demonstrated better results for the In-Vocabulary words; however, DC TTS indicated a higher performance of the Out-Of-Vocabulary words synthesis.

ID: 1017

NLP-Based Customer Loyalty Improvement Recommender System (CLIRS2)

Structured data on customer feedback is becoming more costly and timely to collect and organize. On the other hand, unstructured opinionated data, e.g., in the form of free-text comments, is proliferating and available on public websites, such as social media websites, blogs, forums, and websites that provide recommendations. This research proposes a novel method to develop a knowledge-based recommender system from unstructured (text) data. The method is based on applying an opinion mining algorithm, extracting aspect-based sentiment score per text item, and transforming text into a structured form. An action rule mining algorithm is applied to the data table constructed from sentiment mining. The proposed application of the method is the problem of improving customer satisfaction ratings. The results obtained from the dataset of customer comments related to the repair services were evaluated with accuracy and coverage. Further, the results were incorporated into the framework of a web-based user-friendly recommender system to advise the business on how to maximally increase their profits by introducing minimal sets of changes in their service. Experiments and evaluation results from comparing the structured data-based version of the system CLIRS (Customer Loyalty Improvement Recommender System) with the unstructured data-based version of the system (CLIRS2) are provided.

ID: 597

From Stances’ Imbalance to Their Hierarchical Representation and Detection

Stance detection has gained increasing interest from the research community due to its importance for fake news detection. The goal of stance detection is to categorize an overall position of a subject towards an object into one of the four classes: agree, disagree, discuss, and unrelated. One of the major problems faced by current machine learning models used for stance detection is caused by a severe class imbalance among these classes. Hence, most models fail to correctly classify instances that fall into minority classes. In this paper, we address this problem by proposing a hierarchical representation of these classes, which combines the agree, disagree, and discuss classes under a new related class. Further, we propose a two-layer neural network that learns from this hierarchical representation and controls the error propagation between the two layers using the Maximum Mean Discrepancy regularizer. Compared with conventional four-way classifiers, this model has two advantages: (1) the hierarchical architecture mitigates the class imbalance problem; (2) the regularization makes the model to better discern between the related and unrelated stances. An extensive experimentation demonstrates state-of-the-art accuracy performance of the proposed model for stance detection.